基于混合时频域特征的卷积神经网络心律失常分类方法的研究

吕杭; 蒋明峰; 李杨; 张鞠成; 王志康

doi:10.12263/DZXB.20211181

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基于混合时频域特征的卷积神经网络心律失常分类方法的研究

学术论文 | 更新时间：2025-12-08

- 基于混合时频域特征的卷积神经网络心律失常分类方法的研究
- Research on Arrhythmia Classification by Using Convolutional Neural Network with Mixed Time-Frequency Domain Features
- 电子学报 2023年51卷第3期页码：701-711
- 作者机构：
  
  1.浙江理工大学计算机科学与技术学院，浙江杭州 310018
  2.浙江大学医学院附属第二医院，浙江杭州 310009
- 作者简介：
  
  [ "吕杭男， 1997年8月出生于湖北省黄冈市，现为浙江理工大学研究生. 研究领域方向为医学影像处理." ]
  [ "蒋明峰（通讯作者）男， 1977年5月生于江西丰城，现为浙江理工大学计算机科学与技术学院教授、博士生导师，主要研究方向为计算机医学图像处理、生物医学信号处理．" ]
  [ "李杨男，1986年05月出生于辽宁抚顺，现为浙江理工大学计算机科学与技术学院讲师、硕士生导师，主要研究方向为计算机医学图像处理、计算机视觉. E-mail: dr.yangli@outlook.com" ]
  [ "张鞠成男，1989年10月出生，山东潍坊人，工程师，主要研究方向为MRI关键技术和心脏电生理研究. E-mail: jucheng@zju.edu.cn" ]
  [ "王志康男，1970年5月出生，浙江杭州人，研究员，现为浙江大学校医院副院长，主要研究方向为生物医学信号处理. E-mail: 2192009@zju.edu.cn" ]
- 基金信息：
  
  浙江省科技厅重点研发项目(2020C03060);国家自然科学基金(62101497);浙江省自然科学基金-数理医学学会联合基金重点项目(LSZ19F010001)
- DOI：10.12263/DZXB.20211181
  中图分类号： TP391.41
- 收稿：2021-08-30，
  
  修回：2022-07-09，
  
  纸质出版：2023-03-25
- 稿件说明：
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吕杭,蒋明峰,李杨等.基于混合时频域特征的卷积神经网络心律失常分类方法的研究[J].电子学报,2023,51(03):701-711.

LÜ Hang,JIANG Ming-feng,LI Yang,et al.Research on Arrhythmia Classification by Using Convolutional Neural Network with Mixed Time-Frequency Domain Features[J].ACTA ELECTRONICA SINICA,2023,51(03):701-711.
吕杭,蒋明峰,李杨等.基于混合时频域特征的卷积神经网络心律失常分类方法的研究[J].电子学报,2023,51(03):701-711. DOI： 10.12263/DZXB.20211181.

LÜ Hang,JIANG Ming-feng,LI Yang,et al.Research on Arrhythmia Classification by Using Convolutional Neural Network with Mixed Time-Frequency Domain Features[J].ACTA ELECTRONICA SINICA,2023,51(03):701-711. DOI： 10.12263/DZXB.20211181.

摘要

心律失常是常见的心血管疾病之一，目前很多方法通过计算机辅助系统对心电图进行分析以识别心律失常，但由于大多数心律失常数据样本较少，计算机辅助系统识别心律失常效果不佳.本文提出了一种基于混合时频域分析特征提取的卷积神经网络方法，该方法提取心电图的RR间期时域特征、希尔伯特-黄变换提取的频域特征和连续小波变换提取的时频域联合特征，经过特征融合后输入卷积神经网络训练分类模型，并采用Focal Loss作为网路的损失函数，实现对心律失常的分类.本文使用MIT-BIH（Massachusetts Institute of Technology-Boston's Beth Israel Hospital）心律失常数据库验证本文提出方法对4类心电数据分类的结果，实验结果表明，与现有的分类算法相比，本文所提出的混合时频域特征方法能有效提升心律失常分类的准确性.

Abstract

Arrhythmia is one of cardiovascular diseases

and many methods are used to analyze electrocardiogram by computer-aided system to identify arrhythmia. However

most of the data samples of arrhythmia are small

and the computer-aided system is not effective in identifying arrhythmia. In this paper

a mixed time-frequency domain feature extraction method is proposed for arrhythmia classification by using convolution neural network method. The fused features consist of the time domain characteristics from the RR interval

frequency domain characteristics from hilbert-huang transform

and joint time-frequency domain features extracted from continuous wavelet transform. Then the fused features are used as an input to the convolution neural network for training classification model

and the focal loss is used as the loss function of the training model

so as to realize the arrhythmias classification. In addition

the MIT-BIH （Massachusetts Institute of Technology-Boston's Beth Israel Hospital） arrhythmia database is used to verify the performances of the proposed method for arrhythmias classification of four types of ECG (Electrocardiograph) data. Experimental results show that compared with the existing classification algorithms

the proposed method improves the F1 of class obviously.

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references

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